Health research, clinical registries, electronic health records

how do they (if at all!) all come together?

Koray Atalag MD, PhD, FACHIk.atalag@auckland.ac.nz

Vice Chair HL7 New ZealandopenEHR Localisation Program Leader

Health Information Standards Organisation (HISO) Committee MemberNHITB Sector Architects Group Member

AgendaRegistry definedWhat role does EHR play?openEHRNIHI examplesConclusions

Registry definedAn organised system that uses observational study methods to collect uniform data(clinical and other) to evaluate specified outcomes for a population defined by a particular disease, condition, or exposure,and that serves a predetermined scientific, clinical or policy purpose(s).

GliklichR, Dreyer Ne. Registries for Evaluating Patient Outcomes: A User's Guide Prepared by Outcome DEcIDECenter[Outcome Science, Inc. dbaOutcome] under Contract No. HHSA290200500351TO1). Rockville, MD: Agency for Healthcare Research and Quality, 2007; Publication No. 07-EHC001-

Clinical Registries Register / Registry Clinical (+quality) / disease / patient / incidence / screening etc.

Repository of individuals with certain conditions/characteristicsEase of access to important infoTrack clinical processes & (risk adjusted) outcomes Longitudinal history of correspondences & interventionsPrompt / feedback to participants and providersData linkages & Reporting

Supporting clinical practice◦ Screening, risk prediction, intervention/recall, safety monitoring

Clinical quality improvement◦ Organisations, clinicians, policy makers

Research & education

Why do we need them?Because we don’t have the mighty EHR!Registries are a ‘quick fix’ to some ‘can’t wait’ type

problems / for ‘quick wins’; capturing◦ observations, diagnoses, procedures, clinical processes and most

importantly outcomesProvide an infrastructure on which intervention studies

can be established with relative ease.Who get’s a registry?

◦ Those with funding of course! Clinical significance / popularity (eg. CVD, diabetes) Well established network/specialised (e.g. Spina Bifida) national/intl policies (MoH / WHO – cancer etc.) leadership / persistence / charisma / luck (GDM?)

Around the world & NZA lot of them!Overarching principles / regulations /

minimal standards Shared resources (hosted by dedicated

organisations / infrastructure)

A growing number of themAll go own ways – (under privacy rules)Hosted/curated by source groups with limited

technical/data management resources

Typical UsesIncidence/prevalence of diseases/conditions in

populations & monitor trends/survival rates over time

safety & quality of products and treatments

clinical and/or cost effectiveness of treatment (including drugs, devices and procedures) across a population

provide denominator & vehicle for interventional studies

and sometimes decision support too!

Electronic Health Records (EHR)All directly recorded or derived information

about an individual within healthcare context in electronic form

It is called many names – EHR, EMR, PHR, CPR, EPR, CBPR, AMR, EHCR, ...

Different perceptions: function, purpose, disease, place etc.

Ref: Ed Hammond

What does the EHR Contain?

DATA

Person-centredComprehensiveLongitudinalOrganizedHigh data integrityTimelyStructuredSemantically coherentShareableTrustable and accountableSecure and private

Ref: Ed Hammond

What does the EHR Provide?

Information for

Direct patient careEffective decision supportPrevention of medical errorsImproved quality of careBetter clinical communicationEnabling shared careEvidence based careCost effective careWorkflow managementBio-surveillanceResearchEpidemiologyBilling/reimbursement/health policy/planning

Ref: Ed Hammond

referral

orderresult

discharge

referral

order result

referral

orderresult

| Chest infection | GP review| GP visit | Back to foot clinic

Main GP

Foot ulcer foot clinic (hospital)

Hospital1

Diabetolog

See specialist

LABImaging

| Imaging| Renal function test

| Stroke – hospital

Hospital2

GP2

See other GP on holiday | CT scan

Therapist

| Rehabilitation

Fragmented / non-interoperable data

discharge

referral

Where’s EHR?

© Thomas Beale

How should EHR Work?

referralhospital

Diabetol.

Main GP

DI & path

hospital2

GP2

Soc. worker

discharge

referral

order

order result

discharge referral

orderresult

referral

result

EHR VISIBILITY

S h a r e d C a r e, L o n g i t u d i n a l, p a t i e n t – c e n t r e d EHR

The Patient© Thomas Beale

Barriers to EHR AdoptionLarge initial investment, unfit funding modelsPoor user acceptance (workload?)Privacy concernsLack of solid evidence?Fear & reluctance for the unknownPolitical/societal ignoranceMedico-legal issuesRisky business (for vendors/purchasers)Lack of common information / processes....Interoperability

Types of Interoperability Technical Interoperability: systems can send and receive data successfully.

(ISO: Functional/Data Interoperability)

Semantic Interoperability: information sent and received between systems is unaltered in its meaning. It is understood in exactly the same way by both the sender and receiver.

Process Interoperability: the degree to which the integrity of workflow processes can bemaintained between systems. (This includes maintaining/conveying information such as user roles between systems)

(HL7 Inc.)

If the Banks Can Do It, Why Can’t Health?Clinical data is wicked:

◦ Size (breadth, depth) and complexity◦ >300,000 concepts, 1.4m relationships in SNOMED◦ Variability of practice◦ Diversity in concepts and language◦ Conflicting evidence◦ Longevity◦ Links to others (e.g. family)◦ Peculiarities in privacy and security◦ Medico-legal issuesIt IS critical…

Can Clinicians Agree on Single Definitions of Concepts?

“What is a heart attack?”- 5 clinicians: ~2-3 answers – probably more!

“What is an issue vs. problem vs. diagnosis?”- No consensus for conceptual definition for years!

BUTThere is generally agreement on the structure and attributes of information

to be captured

Problem/Diagnosis name Status Date of initial onset Age at initial onset Severity Clinical description Date clinically recognised

Anatomical location Aetiology Occurrences Exacerbations Related problems Date of Resolution Age at resolution

Diagnostic criteria

Acknowledgement: Sam Heard

Interoperability Standards• Lower/Technical levelPhysical & Data

Standards

• Syntax & SemanticsTerminology Standards

• Sharing & WorkflowMessaging Standards

• Structure & ProcessingContent Standards

SNOMEDICDGALENLOINCATC

UN/EDIFACTHL7 v2 & v3

HL7 (CDA, CCR)openEHRISO/CEN 13606

TCP/IP, HTML, XMLWebservices, SOACORBA, SSL

Why bother?(with a standard structured Medication model)

“If you think about the seemingly simple concept of communicating the timing of a medication, it readily becomes apparent that it is more complex than most expect…”

“Most systems can cater for recording ‘1 tablet 3 times a day after meals’, but not many of the rest of the following examples, ...yet these represent the way clinicians need to prescribe for patients...”

Dr. Sam Heard

Example: Medication timing

Acknowledgement: Sam Heard

Medication timing – and more!!

Acknowledgement: Sam Heard

Medication timing cont.

Acknowledgement: Sam Heard

Medication timing – cont.

Acknowledgement: Sam Heard

Medication timing – even more!

Acknowledgement: Sam Heard

Open source specs & software for representing health information and person-centric records◦ Based on 18+ years of international implementation experience

including Good European Health Record Project◦ Superset of ISO/CEN 13606 EHR standard

Not-for-profit organisation - established in 2001 www.openEHR.org

Extensively used in research Separation of clinical

and technical worldsBig international community

Logical building blocks of EHR

Compositions

EHR

Folders

Sections

Clusters

Elements

Data values

Entries

Patterns in Health Information

Actions

Published evidence base

Personal knowledge

Evaluation

Observations

Subject

InstructionsInvestigator’s agents(e.g. Nurses, technicians, other physicians or automated devices)

Clinician measurable or observable

clinically interpreted findings

order or initiation of a workflow process

Recording data for each activity

Administrative Entry

Acknowledgement: openEHR

Example Model:Blood Pressure Measurement

Archetype Editor

It’s REFERENCE LIBRARY (of reusable clinical information models)

Data & meta-data definitions (data dictionary) Relationships & clinical terminology

Usage of the Content Model

What about secondary use?Interoperability for clinical information

systems – great◦But what about population health & research?

Research data also sits in silos – mostly C Drives or even worse in memory sticks!

Difficult to reuse beyond specific research purpose – clinical context usually lost

No rigour in handling and sharing of data

Exploiting the Content Model for Secondary Use Atalag K. Using a single content model for eHealth interoperability and secondary use. Stud Health Technol Inform. 2013;193:282–96

Single Content Model

CDA

FHIR

HL7 v2/3

EHR Extract

UML

XSD/XMI

PDF

Mindmap

PAYLOAD

System A

Data Source A

MapTo

Content Model

System B

Data Source B

Native openEHR Repository

Secondary Use

MapTo

Content Model

Automated Transforms

No Mapping

Shared Health Information Platform (SHIP)

Gestational Diabetes Registry Development

in CMDHB

Dr. Koray Atalag MD, PhD, FACHI (National Institute for Health Innovation)

Dr. Carl Eagleton MBChB, FRACP (Counties Manukau District Health Board)

Karen Pickering (Diabetes Projects Trust)

Aims 100% successful screening of women for type 2

diabetes (T2DM) within 3 months after a pregnancy with GDM

Annual screening of all women for new onset T2DM

Early warning to healthcare providers (GPs, Maori/Pacific Health, others) about GDM history in subsequent pregnancies

Gestational Diabetes Mellitus (GDM)GDM is characterised by glucose intolerance with

onset or first recognition during pregnancy & is identified by an oral glucose tolerance test (OGTT)

A repeat OGTT performed 6 weeks post-partum checks for resolution ◦ If normal, an annual fasting glucose or glycosylated

haemoglobin (HbA1c) screening test is recommended for T2DM, according to New Zealand (NZ) guidelines.

Opportunities & Motivation for the Registry

Long term consequences can be prevented by regular screening for early detection of T2DM or high CVD risk◦ CMDHB found 20% of women with a history of GDM were not

follow-up tested in a 4 year period; (37% for 2 year period)◦ Sending out reminders improve adherence / better compliance with

screening recommendations

Risk of developing T2DM can be substantially reduced by early identification of women at high risk + targeted lifestyle & pharmacological interventions

Registry can also be used to drive clinical quality improvement and enhance patient safety ◦ by identifying variations in processes and clinical outcomes.

Main ConsiderationsPrivacy / Confidentiality

◦ Privacy Act 1993 and Health Information Privacy Code 1994 (“HIPC”)◦ Recent changes to offshore hosting◦ Connected Health secure network

Security / Recovery / Availability◦ Univ. of Auckland’s secure IT infrastructure

IT standards & components◦ W3C, Microsoft Net, SQL Server, Angular JS◦ HISO Interoperability Reference Architecture◦ openEHR

Existing systems◦ CMDHB: Maternity CIS & others◦ Regional/National: MoH datamart? VDR, PMS, Shared Care etc.

GDM Registry PathwayEntr

y

• Referral from primary care with a diagnosis of GDM

Education

• Attendance at Group Session• Registry information supplied

Consent

• Attendance at DiP Clinic• Consent obtained and entry into the registry

Postpartum

• 6 week OGTT request or 3 month HbA1c• GP & Patient advised of results

Annual

• Annual HbA1c with copy to primary care• GP & Patient advised of results

Next time

• Positive pregnancy test detected in Testsafe• Requesting healthcare provider advised of Diabetes history by the Registry

Regi

stry

Dire

cted

Golden principle: Minimal data entry, Maximal reuse!

Technical DevelopmentUsed an international (and HISO) standard:

◦ Consistent dataset◦ Interoperability / integration◦ Manage change over time

Used a Web-based data set development tool to review & finalise

Automatically converted dataset into “software code” [domain objects]

Built on NIHI’s clinical data repository framework

The Dataset

The Registry SystemThree main parts (exc. System admin)

◦ Demographics◦ Clinical view + entry◦ Intervention

Role based access: clinical and/or adminEntry status:

◦ Temporary: record still being populated (import/data entry), not included in actions

◦ Active: records visible to all & complete◦ Inactive: only admin can see, suspended/opt-out

Enter a new participant Activate Enter / Update clinical data Interventions preview

(ANZACS-QI) New Zealand Acute Coronary Syndrome Quality Improvement and Interventional Cardiology Registry

EHR Providing a Canonical Representationso we know what kind of info goes into which bucket!

Dem

ogra

phic

s

Clin

ical

Enc

ount

er

Vita

l Sig

ns

Med

icati

ons

Dia

gnos

es

Dia

gnos

tic T

ests

Inte

rven

tions

Fam

ily H

isto

ry

Past

His

tory

Phys

ical

Exa

m

Gen

etics

Life

Sty

le

etc.

etc

. etc

.

Subject A

Subject B

Person-Centric Record Organisation

NZ AddressEthicity1,2.Whanau

USAddressStateNext of kin

GP visitFlu-likePHO enrolm.

Hospital adm.DiabetesPriv insurance

BP 130/90HR 90T: 38.5 C

BP 120/70 (24 hour avg)HR 70T: 37 C

Rx ADispenseAdminister

Rx BDispenseAdminister

Dx 1Dx 2etc.

Diabetes Dx-Type-Severity-Course etc.

Routine BloodUrineX-Ray

Specific blood testUrine cultureGenomic assayRetinography

Rx

Fluid TxInsuline injInfection TxPsychologic

N/A

Pedigree

N/A

Chronic

Routine

DetailedFoot and eyes

N/A N/A

DNA Seq.Assays

Low sugarExercise

Shared Archetypes

Each finding usually depends on other – clinical context matters!

Bottom lineWe may not have EHR now....but

by using openEHR to represent our clinical information we are leveraging some of the benefits of EHR today, including◦ Expressivity, clinical context, meta-data support◦ Interoperability◦ Semantic querying (easy + fast)◦ Tooling and international content◦ Standards compliance

and future-proofing registry data!